Iterative Multiple Hypothesis Tracking With Tracklet-Level Association

Hao Sheng, Jiahui Chen, Yang Zhang, Wei Ke, Zhang Xiong, Jingyi Yu

Research output: Contribution to journalArticlepeer-review

59 Citations (Scopus)

Abstract

This paper proposes a novel iterative maximum weighted independent set (MWIS) algorithm for multiple hypothesis tracking (MHT) in a tracking-by-detection framework. MHT converts the tracking problem into a series of MWIS problems across the tracking time. Previous works solve these NP-hard MWIS problems independently without the use of any prior information from each frame, and they ignore the relevance between adjacent frames. In this paper, we iteratively solve the MWIS problems by using the MWIS solution from the previous frame rather than solving the problem from scratch each time. First, we define five hypothesis categories and a hypothesis transfer model, which explicitly describes the hypothesis relationship between adjacent frames. We also propose a polynomial-time approximation algorithm for the MWIS problem in MHT. In addition to that, we present a confident short tracklet generation method and incorporate tracklet-level association into MHT, which further improves the computational efficiency. Our experiments on both MOT16 and MOT17 benchmarks show that our tracker outperforms all the previously published tracking algorithms on both MOT16 and MOT17 benchmarks. Finally, we demonstrate that the polynomial-time approximate tracker reaches nearly the same tracking performance.

Original languageEnglish
Article number8533372
Pages (from-to)3660-3672
Number of pages13
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume29
Issue number12
DOIs
Publication statusPublished - Dec 2019

Keywords

  • Multiple object tracking
  • iterative maximum weighted independent set
  • multiple hypothesis tracking
  • polynomial-time approximation
  • tracking-by-detection

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